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NVIDIA

Designs the chips and software that power AI

Senior Solutions Architect - Robotics Foundation Model Training

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeRemote

About the Job

Requirements

  • A Master of Science, PhD, or equivalent experience in Computer Science, Artificial Intelligence, Electrical Engineering, Computer Engineering, Robotics, or a related field.
  • At least 5 years of industry or research experience in deep learning, distributed computing, or large-scale model training.
  • Hands-on experience training or optimizing multimodal or foundation models, ideally in robotics settings.
  • Experience across the AI model lifecycle, including pre-training, supervised fine-tuning, reinforcement learning or other post-training methods, evaluation, and model optimization.
  • Expertise in distributed training techniques, including data, model, and pipeline parallelism, sharding, and checkpointing, on multi-GPU or multi-node systems.
  • Expertise with multimodal training frameworks such as PyTorch, NVIDIA NeMo, JAX, or Hugging Face Transformers.
  • Experience building or working with high-throughput data pipelines for large-scale training, including storage bandwidth, network throughput, and preprocessing such as decoding, tokenization, and batching.
  • Ability to collaborate effectively across researchers, engineers, and executives.

Responsibilities

  • Engage with researchers and machine learning engineers to architect and optimize end-to-end training workflows for robotics foundation models, including World Models, VLAs, and WAMs.
  • Build proof-of-concepts, reference architectures, and agentic workflows to accelerate experimentation, benchmarking, and model improvement for NVIDIA robotics open model platforms such as Cosmos and GR00T.
  • Scale pre-training, fine-tuning, and reinforcement learning workloads across multi-GPU and multi-node systems, improving utilization, throughput, and memory efficiency.
  • Identify and eliminate data pipeline bottlenecks across storage, networking, preprocessing, and data loading for multimodal datasets such as video, sensor data, and trajectories.
  • Collaborate with NVIDIA product and engineering teams to provide feedback that shapes future Physical AI platforms.

Desired Qualifications

  • Familiarity with NVIDIA AI and robotics platforms such as Cosmos, GR00T, NeMo, Isaac Sim, and Isaac Lab.
  • Experience with robotics AI workloads, including reinforcement learning in simulation and synthetic data generation.
  • Experience profiling and optimizing workloads using tools such as Nsight Systems, Nsight Compute, or PyTorch Profiler.
  • Demonstrated impact improving training efficiency and scaling performance.
  • Experience building agentic workflows for automated experimentation, model evaluation, data analysis, or research acceleration.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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